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Scientia Horticulturae 138 (2012) 151–158

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Scientia Horticulturae

journa l homepage: www.e lsev ier .com/ locate /sc ihor t i

ualitative and quantitative aspects of olive production in relation to climate inouthern Italy

abio Orlandi ∗, Tommaso Bonofiglio, Bruno Romano, Marco Fornaciariepartment of Applied Biology, University of Perugia, Borgo XX Giugno 74, 06121 Perugia, Italy

r t i c l e i n f o

rticle history:eceived 20 January 2012ccepted 21 February 2012

eywords:livegroclimatologyrop growthrop qualitylant–environment interactions

a b s t r a c t

We considered here olive production according to the main relationships between the quantitative (fruitproduction) and qualitative (oil quality) data and the meteorological parameters through the annualseason. We carried out pollen monitoring analyses over a 12-year period, to initially define the maindynamics of pollen release into the atmosphere in a typical olive production area in southern Italy(Fasano, Puglia). Pollen release was generally determined by the meteorological factors in the periodbefore flowering, with effects on both pollen amounts and timing of flowering (early or delayed). Theinteractions between these pollen and meteorological parameters were investigated using principal com-ponent analysis. This revealed clusters of independent variables during the biological cycle of the olivethat highlighted the relationships between annual pollen production and future fruit production, with a

high degree of interaction between environmental factors and final olive oil quality. This led us to searchfor statistical relationships between quality parameters of the oil (acidity, oxidative degree, fatty-acidcomposition) and meteorological parameters (mean monthly temperatures, rainfall, humidity, winds,solar radiation, evapotranspiration during post-flowering/pre-harvest). The water stress, as defined bythe potential evapotranspiration, has a large influence on acidity and fatty-acid content of the olive oil

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. Introduction

Studies of fruit production of the olive Olea europaea L. needo include the various phenological phases of the plant, the mostmportant of which is flowering. Furthermore, the physiologicaltate of the olive tree and the trends in the main meteorologicalariables (temperature and precipitation) need to be considered.uring flowering, meteorological conditions are important, as theyirectly determine the number of growing fruits that can reach thenal harvest period (Fornaciari et al., 2005).

Another factor that conditions pollination of the olive that isidespread is intraspecies auto-incompatibility (Reale et al., 2009).

he olive does not show morphological obstacles that prevent self-

ollination, as the flowers are hermaphrodite and maturation of theexual organs is concurrent. However, some studies have shown

Abbreviations: CPI, contracted pollen index; EPP, effective pollination period;DD, growing degree-days; Hum, humidity (%); PCA, principal component analysis;ET, potential evapotranspiration; PI, pollen index; Rad, solar radiation; Rain, pre-ipitation (mm); Tmax, maximum temperature (◦C); Tmin, minimum temperature◦C); Wind, wind intensity at 10 m.∗ Corresponding author. Tel.: +39 075 5856411; fax: +39 075 5856598.

E-mail address: fabor@unipg.it (F. Orlandi).

304-4238/$ – see front matter © 2012 Elsevier B.V. All rights reserved.oi:10.1016/j.scienta.2012.02.029

vide further support of the need for quality irrigation in olive production.© 2012 Elsevier B.V. All rights reserved.

various types of intraspecies sterility, which can be cytological,morphological, physiological or factorial (Reale et al., 2006).

As well as the quantity of fruit, the qualitative components ofolive oil produced can be influenced by the environmental con-ditions of the year of production (Lombardo et al., 2008). Thisrelates to the absolute variations in fatty acids, and the relation-ships between these individual components, such as the oleicacid/linoleic fatty-acid ratio, and the ratio between oleic acid andthe sum of palmitic and linoleic acids (D’Imperio et al., 2007). Thephase of maturation of the olive fruit influences not only its acidcomposition, but also the composition of its minor constituents,and particularly its phenolic and volatile compounds. Thus, factorsthat affect the evolution of maturation of the drupe can also affectthe qualitative characteristics of the resulting olive oil (Fiorino andNizzi Griffi, 1991).

This variability in acid composition has been correlated to thetemperature sum of the period from fruit setting to fruit matu-ration. The high temperatures during this phase that arise in hotseasons and environments can result in decreased oleic acid con-tent, which is accompanied by increased palmitic and/or linoleicacids (Lombardo et al., 2008). A very high temperature sum also

tends to reduce total polyphenol content (Ripa et al., 2008). Simi-larly, in cooler areas, a positive correlation has been shown betweenthe temperature sum from August to October and the total polyphe-nol content (Tura et al., 2008).

1 orticulturae 138 (2012) 151–158

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A water deficit during initial development of the fruit (in Junen the northern hemisphere) can result in a decrease in the size ofhe cells of the mesocarp that cannot be recovered, except, at leastn part, if the plants are regularly irrigated in the following stagesServili et al., 2004). Water deficit affects fruit maturation, whichccurs earlier and more rapidly, and can result in more intense pre-arvest fruit fall (Inglese et al., 1996). However, a number of studiesave shown that the water state of the plant has marginal, if any,ffects on free acidity and peroxide value of the olive oil producedServili et al., 2007).

The water state of the plant also has marked effects on con-entrations of volatile compounds in the oil. Thus, oil from plantsrown without irrigation, as opposed to those with, can beore bitter and biting to the taste (Servili et al., 2007). Plants

rown under conditions of water stress therefore tend to pro-uce oils that are more full bodied and strong in their taste,ith strong bitter and biting notes, but that are relatively less

romatic.The content of volatile compounds in the olive reaches its high-

st levels in the initial phases of darkening of the fruit skin (Famianit al., 2002). The total content of phenolic compounds increases inhe first phase of maturation of the olive, and then diminishes witharkening of the skin and the fruit pulp, and as a function of theaturation process of the cultivar (Brenes et al., 1999; Uceda et al.,

999; Salvador et al., 2001; Baccouri et al., 2008).The maturation state of the fruit at harvest is probably the

reatest source of variability of the oil characteristics, while for cul-ivation techniques, irrigation appears to have the strongest effectsn the composition of the oil produced, and in particular, on thehenolic and volatile compounds (García et al., 1996; Inglese et al.,996).

In the present study, an investigation was carried out on anlive cultivation area in Puglia (southern Italy). Olive growing inuglia is the most widespread tree cultivation, and is of great eco-omic importance. The area occupied by olive cultivation is around58,000 ha, with over 44 million trees, and with an annual produc-ion of 11–12 million quintals of olives, and 2.6 million quintals ofil. Indeed, olive growing in Puglia represents about a third of thathroughout Italy, making Puglia the largest olive-growing region intaly.

This study thus involved olive pollen monitoring from 1999 to010 (inclusive), with the aim of understanding the main floweringynamics through pollen release into the atmosphere. Evolution ofollen release is generally determined by the meteorological fac-ors during the period that precedes the anthesis of the olive, withhe double effect of conditioning the quantity of the pollen releasednd promoting earlier or later flowering (Bonofiglio et al., 2009;rlandi et al., 2005, 2010a). Through quantitative sampling of oliveollen, previous studies have shown close associations betweenhe annual production of pollen and the consequent olive yieldFornaciari et al., 1998; Galán et al., 2004; Ribeiro et al., 2008). Thisrovides the possibility of very early estimates of fruit quantity ofhe harvest, to allow early planning of harvesting and commercial-sation of the production (Orlandi et al., 2010b).

A further main objective of the present study was to determinehe relationships between meteorological trends, olive flowering,nd final fruit production in this study area that is representa-ive of southern Italy, and where it was possible to also recordogether all of the biological, meteorological and productionarameters.

Finally, the interactions defined here between environmen-al factors (mainly meteorological variables) and the final quality

f the olive oil has allowed us to define the statistical relation-hips between the various oil qualitative parameters (free acidity,xidation level, acid component) and the meteorological parame-ers (temperature, precipitation, humidity, wind, solar radiation,

Fig. 1. The Puglia (southern Italy) study area, showing the olive orchard (open circlewith cross) and meteorological station (open square) locations.

potential evapotranspiration [PET]) recorded during the post-flowering, pre-harvest period, over these 12 consecutive years ofharvest.

2. Materials and methods

The present study took into consideration an olive-growing areaaround the town of Fasano (Puglia, southern Italy), where a typicalolive farm was identified as representative of local olive growing(Fig. 1). Pollen monitoring was carried out on these olive orchardsevery year for 12 consecutive years (1999–2010), from May to June.The annual olive yields were also recorded, along with the qualita-tive data for the oil extracted from the fruit harvested.

The olive orchards investigated cover around 10 ha, with6 m × 6 m planting, and with around 2600 trees. The olive culti-vars present are: 40% ‘leccino’, 30% ‘coratina’, and 30% ‘picholine’.Once harvested, the olive fruits were taken to an olive mill and

milled within ca. 24 h, to avoid fruit deterioration. The study areais located within a territory characterised by a relief that decreasestowards the Adriatic coast, with altitudes from 350 m asl down to110 m asl.

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.1. Pollen monitoring for the flowering investigation

Over the more recent decades, studies of atmospheric particlesf biological origin have taken on great importance, with particu-ar attention to sources, diffusion, dispersion, and transport, along

ith the impact of these particles on animals, plants and man. Inhe present study, aerobiological sampling was carried out usinghe ‘volumetric’ method. This is based on capturing pollen andther biological particles in the atmosphere by depression impactCandau et al., 1998; Fornaciari et al., 2000; Galán et al., 2004;arcia-Mozo et al., 2009). The classical instrument that uses thisrinciple is the Hirst spore trap (Hirst, 1952). The model used in theresent study was the VPPS 2000 Lanzoni, a Hirst-type suction slit

mpactor that was operated continuously at a flow rate of 10 l/min.his was located inside the olive grove of the farm under study,llowing coincidence of pollen release and detection. The pollenrains were thus monitored over a 12-year period using the inter-ational standard sampling procedures (Galán et al., 2007; Ogdent al., 1974; Smith, 1990; Solomon, 1984).

In the study area, the flowering of the olive trees was monitoredccording to the mean daily pollen concentrations (mean pollenrains/m3 air), to identify the main phases of pollen release. Thisnalysis provided the following yearly data: dates of the start andnd of flowering; duration of the full flowering period; pollinationntensity; and number of days from start of flowering to maximumollen detection (the peak days). These data were further analysedo provide the pollination markers: the pollen index (PI), as the sumf the mean daily concentrations of pollen grains recorded duringhe entire period of flowering; the contracted pollen index (CPI), ashe sum of the mean daily concentrations to the day of maximumollen release; and the effective pollination period (EPP), as theum of the mean daily concentrations over the last 4 days beforehe flowering peak.

.2. Meteorological data

The meteorological station used as representative of the studyrea is part of the Regional Agrometeorological Network for the arearound Fasano, and it is situated 3–4 km from the olive orchardsnder study (Fig. 1). The daily meteorological data were recordedor the full 1999–2010 period, and included: mean, minimum and

aximum temperatures; mean, minimum and maximum humidi-ies; precipitation; wind direction as vectorial mean at 10 m; meannd maximum wind intensity at 10 m; and solar radiation. More-ver, the temperature sum and the potential evapotranspirationPET) were calculated (Fornaciari et al., 1998, 2005).

Following identification of the date of the start of pollination,he relationships between spring temperature trend and devel-pment of flower structures (Caruso et al., 1992) were definedhrough calculation of the growing degree days (GDD). The sim-le triangulation method was used, considering the minimum andaximum temperatures of day ‘n’ and the minimum temperature

f day ‘n + 1’ (Anderson et al., 1992; Baskerville and Emin, 1969).he threshold temperature above which the hours of warmth wereccumulated for the olive species was set as 7 ◦C, as the study areas characterised by a meso-Mediterranean climate where optimumhreshold ranges are indicated between 5 ◦C and 7 ◦C. Other stud-es have indicated the various correlation values from 6 ◦C to 9 ◦CBongi, 2002; Galán et al., 2005; Orlandi et al., 2010b).

For evapotranspiration, the PET values were calculated usinghe open-access program PMday, based on the standardisedenman–Monteith formula (Hargreaves and Samani, 1982) from

he Environmental Water Resources Institute of the American Soci-ty of Civil Engineers. This calculates reference evapotranspirationETref) for short (ETos) and tall (ETrs) canopies using daily weatherata. The daily evapotranspiration data were thus calculated from

turae 138 (2012) 151–158 153

the following meteorological reference variables: solar radiation(MJ/m2), wind speed (m/s), minimum and maximum temperatures(◦C), and minimum and maximum humidities (%).

2.3. Qualitative analysis of olive oil

Olive oil is a noble product primarily due to its high percent-age of oleic acid and balanced proportions of polyunsaturated andsaturated fatty acids. Evaluations were carried out on the olive oilobtained on an annual basis for influences of the main environmen-tal factors (the meteorological variables) on the olive oil qualitativeindices, as defined as:

Acidity (g oleic acid/100 g oil): Indicative of the relative levels offree fatty acids that form during enzymatic hydrolysis of triglyc-erides.Peroxide value (mequiv. active O2/kg oil): Indicative of the degreeof primary oxidation.UV absorbance (232 nm, 270 nm): Based on the characteristic spe-cific extinctions of dienes (K232) and trienes (K270).Saponification fraction (%): Including triglycerides formed fromglycerol and fatty acids, which make up more than 98% of olive oil.The most representative of the fatty acids (ca. 99%) are: saturatedpalmitic (C16:0) and stearic (C18:0) acids; monounsaturated palmi-toleic (C16:1) and oleic (C18:1) acids; and polyunsaturated linoleic(C18:2) and linolenic (C18:3) acids.

2.4. Statistical analysis for annual olive yield

The forecasting of fruit yield represents a typical case where‘chemometric methods’ can help, because of the large number ofvariables and their potential autocorrelation. Here, the matrix ofthe variables for principal component analysis (PCA) comprisedthe meteorological variables of temperature, precipitation and rel-ative humidity as yearly means over 12 years for the periods fromOctober to December of the year preceding the olive harvesting(t − 1), and from January to September of the year of the oliveharvesting (t). The variables matrix also considered the indices ofpollen release (PI, CPI, EPP), as biological parameters.

Following PCA, the meteorological variables and pollen indicesthat showed the greatest variance were extracted from the ini-tial complex matrix. The provisional statistical model was thendefined, based on multiple linear regression. In particular, a seriesof normality tests were carried out for the presence/absence of themain assumptions necessary for the regression analysis (which allused the NCSS 2007 statistical software). Through various multipleregressions, we finally defined a ‘parsimonious’ statistical model ofindependent variables that explained the major variance relativeto the dependent variable (the annual olive yield).

2.5. Relationships between olive oil qualitative parameters andmeteorological data

The relationships between the above-cited qualitative param-eters obtained each year from laboratory analyses of the olive oil,and the main meteorological variables of the olive production areawere defined. Here, Pearson correlation analysis was performed,

considering: temperature of the pre-flowering period (fruit matu-ration proxy variable), summer water stress (estimated by the PET),and monthly means of individual meteorological variables duringpost-flowering and pre-harvest.

154 F. Orlandi et al. / Scientia Horticulturae 138 (2012) 151–158

Fig. 2. The full flowering dates (days with peaks of pollen concentrations) of thes

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Table 1Results of the PCA autovalues with their relative percentages of explained variance.

Component Eigenvalue Individual (%) Cumulative (%)

1 17.04 25.06 25.062 14.08 20.71 45.773 8.28 12.18 57.954 7.34 10.80 68.755 6.16 9.06 77.816 5.48 8.05 85.867 3.92 5.77 91.638 3.19 4.69 96.32

tudy area across the study period.

. Results

.1. Olive flowering

The pollen monitoring allowed useful information to bebtained regarding the flowering of this anemophilous species.ig. 2 shows the dates of maximum flowering (days with maxi-um pollen concentrations), which show low annual variability.

hese dates are assumed to be linked to the meteorological trendsn the period preceding maturation of the reproductive structures.he maximum flowering dates varied within 15 days, from 11 Mayn 2001, to 26 May in 2004.

The highest daily pollen concentrations were always recordedn the last 10 days of May. These fell between 21 May and 26 Mayn the years with the highest pollen concentrations (2005, 2006).ig. 3 shows the annual data over the study period for the pollenarkers, as PI, CPI, and EPP. These illustrate that in some years,

arge quantities of pollen were released, with respect to the meanevels.

ig. 3. Annual pollen indices across the study period. PI, pollen index, as the sum ofhe daily concentrations during the flowering period; CPI, contracted pollen index,s the sum of the daily concentrations only to the day of maximum pollen release;PP, effective pollination period, as the sum of the daily concentrations of the fourays preceding the maximum pollen release.

9 1.84 2.71 99.0310 0.66 0.97 100.00

3.2. The olive yield forecasting model

Table 1 gives the results from the PCA, showing the eigenvalueswith their individual and cumulative percentages. The first compo-nent represented more than 25% of the explained variance, whilethe contribution of the first six components provides just over 85%of the information contained in all of the variables considered.

Table 2 shows the variables that have the greatest correlationwith the six factors considered on the basis of the above PCA anal-ysis. The variables are reported in decreasing orders on the basis ofthe level of correlation with each factor itself. The most significantvariables expressed by the first factor are connected to the late-spring to summer evapotranspiration, the summer humidity, andthe date of flowering. The summer period with the most correlatedvariables with the final production is June to September, with thecombination of PET and humidity. The variables linked to the sec-ond factor relate to the pre-flowering months of February to May,and are mainly represented by spring temperature accumulation(GDD; March–May) and maximum and minimum temperatures(February, March). The third factor includes September tempera-ture variables and spring and summer precipitation, as well as thebiological indices PI, CPI, EPP, which demonstrate the importance ofpollen monitoring in the atmosphere to quantify the reproductivepotential of the trees in the study area.

For the remaining three factors (4, 5, 6), further meteorologicalvariables are important to increase the explained variance, withprevalence of parameters linked to the water state of the terrain:precipitation (spring, October), humidity (January), wind (summer)and temperature (spring, summer).

Table 3 gives the full results of the regression analysis. The finalestimated model obtained (Table 3) shows very high explained sig-nificance, as R2 just over 91%, with the adjusted R2 just over 85%.The regression equation considers the pollen variable PI, the dateof flowering, and the sum of the GDD; moreover, these also includesummer evapotranspiration (PET), for June. The regression coef-

ficients for the final olive harvest show positive correlations forincreased pollen released and accumulation of GDD in the periodpreceding flowering. On the other hand, negative coefficients are

Table 2Results of the PCA. Factor structure summary for the meteorological variables cor-responding to the various factors.

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

PET Jun–Aug GDD flo. Rain Jun Rad Jun Wind Jun Rain OctPET May–Jul GDD 31.05 Tmin Sep Rain Apr Hum Jan Tmax AprPET Jul Rad Jul Rain Mar Wind Aug Tmax Aug Rain MayPET Jul–Sep GDD 30.04 Hum Jun Hum JanHum Jul TMax Feb PET Tmin JulHum Aug Tmin Feb Tmax SepPET Aug Rad May CPIPET Sep Tmin Mar Rain JanHum Sep GDD 31.03 � Jun–OctDate flowering TMax Mar PI

F. Orlandi et al. / Scientia Horticulturae 138 (2012) 151–158 155

Table 3Results of the least-squares regression analysis between olive yields (ton) as depen-dent variable and bio-meteorological independent variables.

Parameter Value

Dependent variable Olive fruits (ton)Number of independent variables 4R2 0.9148Adjusted R2 0.8580Coefficient of variation 0.0878Mean square error 62.3851Square root of mean square error 7.8984Mean absolute predicted error 5.907

Indep. variable Regress. coeff. Standard error T-Value Prob. level

Regression equationIntercept 401.3887 79.0319 5.079 0.0023Date flowering −2.5903 0.5137 −5.043 0.0024PET Jun −0.4039 0.1261 −3.202 0.0185GDD 0.1146 0.0355 3.232 0.0179PI 0.0009 0.0002 4.200 0.0057

Estimated model401.3886 − 2.5903 × Date flowering − .4038 × PET Jun + .1145 × GDD+ 8.5675E−04 × PI

Row Actual olive ton Predicted olive ton St. error of predicted

Predicted values with confidence limits of means1 112.190 110.142 4.6152 62.150 68.425 6.8863 127.890 126.087 6.0874 87.150 88.279 4.2835 104.670 112.143 5.6836 64.830 70.257 5.8377 98.530 95.013 4.6548 71.080 77.250 6.6449 102.010 99.187 3.594

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een for the other two variables (flowering date, PET), thus showingegative correlation with olive production. Earlier flowering willhus have positive effects on olive production, as will reduced evap-transpiration. The significance levels are very good for all of theseariables, both meteorological and biological, with the threshold ofhe null hypothesis always less than 2%. The final part of Table 3ives the results of the regression analysis as the predicted values,ith standard errors.

.3. Main qualitative results of the olive oil produced in the studyrea

The results of the qualitative analysis of the olive oil for the sam-les from the olive orchards investigated are shown as the acidity,eroxide value and spectrophotometric constants in Fig. 4, and theatty-acid components in Fig. 5, to illustrate the trends within eachata series.

The top panel of Figure 4 shows the acidity parameter (as %oleiccid), where this is greater than the mean (0.3%) in 2001, 2003,nd 2004. Moreover, there is a visible decreasing trend (R2 = 0.36)ver the study period, thus with lower acidity values from 2005n particular. The middle panel of Fig. 4 shows the peroxide values a marker of the state of oxidative spoiling of the olive oil pro-uced. With a legal limit of 20, there is a decreasing trend (R2 = 0.12)hat oscillates between 5 and 9 over the years, which are normalalues for a good extravirgin olive oil. The bottom panel of Fig. 4

hows the spectrophotometric indices (K232, K270) that allowuantification of the various oxidation products in the oil. Over theears, the K232 shows a decreasing trend (R2 = 0.25), while K270 is

Fig. 4. Acidity (top), peroxide value (middle) and spectrophotometric indices (bot-tom; K232, K270) across the study period.

essentially constant. Of note, their maximum values are 2.50 and0.22, respectively.

3.4. The fatty-acid components

The levels of the fatty acids, as percentages of the total fattyacids, are shown in Fig. 5. These refer solely to the fatty acids ofgreater importance: palmitic, oleic, palmitoleic, stearic, linoleic,and linolenic acids. These are conditioned by the genetic char-acteristics of the various olive cultivars, and by climatic andmeteorological conditions and fruit maturation level.

For palmitic (Fig. 5, top) and stearic (Fig. 5, middle) acids, theserepresent between 10% and 13%, and 1.6% and 2.5%, respectively.Neither of these show trends over the course of the years of thestudy that are statistically significant with respect to the mean.

Among the mono-unsaturated fatty acids, there are oleic (Fig. 5,top) and palmitoleic (Fig. 5, middle) acids. Oleic acid is the mostabundant fatty acid in extravirgin olive oil, and it various here from

increasing trend (R2 = 0.19) over the years of the study. Palmitoleicacid remains relatively stable over the years, generally at just over1.0%.

156 F. Orlandi et al. / Scientia Horticulturae 138 (2012) 151–158

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Table 4Correlation indices between the qualitative characteristics of the olive oil and thetemperature sum from June to October (fruit maturation proxy).

Factor �TMax Jun–Oct �Tmin Jun–Oct

Olives (ton) 0.15 0.37Acidity 0.75 0.65Peroxide value −0.01 −0.06K232 0.30 0.27K270 −0.43 −0.28Palmitic acid −0.20 0.14Palmitoleic acid 0.36 0.58Stearic acid −0.25 −0.05Oleic acid −0.39 −0.63Linoleic acid 0.28 0.57

palmitoleic and linoleic acids. Oleic acid shows a significant nega-tive correlation (as does linolenic acid, although this does not reach

Table 5Correlation indices between the qualitative characteristics of the olive oil and theevapotranspiration from June to September (water stress proxy).

Factor PET Jun PET Jul PET Aug PET Sep PET Mean

Acidity −0.01 −0.21 −0.41 −0.56 −0.36Peroxide value 0.55 0.47 0.35 0.54 0.50K232 0.53 0.27 0.16 0.25 0.29K270 −0.07 0.46 0.23 0.07 0.26Palmitic acid 0.45 −0.26 −0.18 0.25 0.09Palmitoleic acid 0.38 −0.44 −0.42 −0.20 −0.23

ig. 5. The levels of the saturated (top), monounsaturated (middle) and polyunsat-rated (bottom) fatty acids across the study period.

Finally, the bottom panel of Fig. 5 shows the levels of the polyun-aturated fatty acids: linoleic and linolenic. Over this study period,he levels of linoleic acid vary from 6% to 10%, while those ofinolenic acid are around 0.6%, with both essentially stable.

.5. The main relationships between the qualitative parametersf the olive oil and the meteorological data

The correlation analysis between the meteorological variablesnd the qualitative characteristics of olive oil are shown in Table 4.uring fruit maturation, the composition of the oil in the olivendergoes a series of modifications that follow the particularly highre-harvest temperature trends, with a reduction in the phenolicubstances, and an increase in free acidity and peroxide value. Thisherefore provides less protection for the oil during its conserva-ion.

The correlation analysis of the maximum and minimum dailyemperature sum for the period of June to October with respect

o the qualitative characteristics of the oil product demon-trates a direct relationship between pre-harvest temperatureum, free acidity, and palmitoleic (mono-unsaturated) and linoleicpolyunsaturated) acids (Table 4). On the other hand, an evident

Linolenic acid −0.12 −0.01

over-maturation of the olives induced by large summer temper-ature accumulation shows negative correlation with oleic acid, acompound that is less susceptible to oxidation compared to linoleicacid. Moreover, Table 4 shows that the summer temperature accu-mulation that is most closely linked to the qualitative variablesgiven previously is that defined by the minimum temperatures.Therefore, the minimum temperatures have a greater influence onthese parameters than the corresponding maximum temperatures.

Table 5 gives the correlation levels between the summer ‘waterstress’ indicators, as estimated through the PET, and the qualita-tive characteristics of the olive oil produced. Significance is seenfor most of the qualitative variables considered. The summer peri-ods that are mainly correlated with these qualitative analyses areJune and July, and also to a lesser extent (acidity and peroxidevalue), September. The values recorded for August are lower, whichis probably linked to the local meteorological conditions.

Further to these considerations of the relationships betweenthe qualitative characteristics and the maturation state and thesummer water stress, we also analysed the specific relationshipsbetween each meteorological variable and the qualitative char-acteristics in the post-flowering and pre-harvesting periods (Juneto October). Table 6 shows that the precipitation (rain) recordedduring the summer months (July, August), and the humidity inSeptember are positively correlated with free acidity, while there isan inverse correlation between acidity and mean radiation in Julyto September, and the mean PET of August and September.

Table 7 shows the influence of the annual yields (as olive fruit,in tons) on the qualitative characteristics of the olive oil. Thesecorrelation indices show that fruit production has no significantinfluence on free acidity, peroxide value, and spectrophotometricindices (dienes [K232], trienes [K270]). Conversely, it has a signifi-cant influence on the acidic components, and in particular, thosewith the highest positive correlations with fruit production are

Stearic acid 0.28 0.65 0.35 0.31 0.48Oleic acid −0.48 0.21 0.36 0.13 0.14Linoleic acid 0.43 0.12 0.05 −0.01 0.21Linolenic acid 0.17 0.00 −0.12 0.22 0.01

F. Orlandi et al. / Scientia Horticulturae 138 (2012) 151–158 157

Table 6Meteorological variables that are the most correlated with the qualitative characteristics of the olive oil.

Acidity Peroxide value K232 K270

Rain Jul 0.67 Hum Aug −0.58 Rain Sep −0.56 Tmax Sep 0.50Rain Aug 0.53 Wind Sep 0.81 Wind Jul 0.65 Tmin Sep 0.61Hum Sep 0.54 PET Jun 0.54 Wind Sep 0.51 Rain Aug −0.69Rad Jul −0.52 PET Jul 0.46 PET Jun 0.52 Rain Sep −0.60Rad Sep −0.46 PET Sep 0.54 Rad Jul 0.55PET Aug −0.43 PET Jun–Aug 0.55 Rad Aug 0.61PET Sep −0.56 PET Jul–Sep 0.48 PET Jul 0.46PET Jul–Sep −0.43

Palmitoleic acid Oleic acid Linoleic acid Linolenic acid

Tmax Sep −0.42 Tmin Jul −0.45 Wind Jun 0.43 Tmax Sep 0.52Tmin Sep −0.42 Rain Jun 0.57 PET Jun 0.44 Tmin Oct 0.64Rain Jul 0.50 Wind Jun −0.58 PET May–Jul 0.42 Rain Oct −0.45Rain Aug 0.53 PET Jun −0.50

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Rad Jul −0.53Rad Aug −0.41Rad Sep −0.55

ignificance), which is in agreement with data from other studiesBarone et al., 1994).

. Discussion

The present study has revealed some links between the produc-ion processes of the olive and climatic trends.

Considering pollen released, the ‘see-saw’ trend in the indicessed for evaluation of the flowering is interesting to note. In partic-lar, it is important to stress the significant relationships betweenhe quantity of the emitted pollen grains and the final fruit produc-ion, a relationship also seen in the statistical analyses. Indeed, all ofhe predictive models of olive production need to include consider-tion of the PI, CPI or EPP. In terms of the predictive analysis for oliveroduction, these can be summarised as significant relationshipsetween the explicative variables (meteorological and biological)nd the olive production. In particular, through the PCA, it can beeen that the meteorological variables can be grouped into timelusters within the production period that are very well defined.ne cluster of variables of great importance is identified during theeriod of vegetative–reproductive awakening (in spring). Anotherrucial moment for the final production occurs during the sum-er period, when very hot temperatures (e.g. those recorded in

003) can have drastic effects on the small growing fruits, leadingo increased fruit fall. From the biological point of view, moreover,he PI is a good indicator of the first phase of the physiologicalrocess that results in the fruiting of the olive. The implication forhe model is that we can also use this indicator as a proxy of the

onditions before the anthesic period. This can also be used to inter-ret the influence of the meteorological variables in autumn of theear preceding that of the harvesting, which are implicated in thehysiological processes of flower induction.

able 7orrelation matrix between the olive yield (ton) and the qualitative parameters.

Factor Olives (ton)

Olives (ton) 1.00Acidity −0.22Peroxide value 0.26K232 0.26K270 0.04Palmitic acid 0.34Palmitoleic acid 0.64Stearic acid −0.11Oleic acid −0.78Linoleic acid 0.66Linolenic acid −0.34

The identification of these groups of climatic variables in par-ticular periods of the year is of great interest also in terms of theinteractions between the meteorological data and the qualitativecharacteristics of the olive oil. In particular, the great importanceof water stress, evaluated essentially through calculation of evapo-transpiration (the PET), can be seen for the free acidity, as well as forthe fatty acids and their ratios. In general, the summer water stressnegatively influences acidity, while peroxide values and K232 andK270 show direct correlations. As indicated, the acidic componentsare also influenced by the PET, exclusively in the first phase of fruitgrowth (June–July), with correlations between high values of stressand low levels of both palmitoleic and oleic acids. On the otherhand, a direct relationship connects the water stress to the levelsof linoleic and linolenic acids, where higher stress corresponds tohigh levels of these fatty acids. This therefore provides further sup-port for the concept of qualitative irrigation (Servili et al., 2004).The use of irrigation while monitoring for water deficit appears tobe an economically sound and sustainable cultivation practice thathas low environmental impact, and indeed, this is easy to apply inolive orchards that are already equipped for localised irrigation. Theadvantages here are numerous, such as the possibility to improveor diversify the antioxidants and the organoleptic analytical pro-file of extravirgin olive oils. It has been reported in the literaturethat the sensory attributes affected by irrigation are the ‘bitterness’,‘pungency’, and ‘fruitiness’, which are related to the phenol contentin olive oil, which is higher in oils obtained from irrigated oliveorchards (Gómez-Rico et al., 2007; Stefanoudaki et al., 2001). How-ever, a slight decrease in the intensity of these positive attributes isseen through an increase in the amount of water delivered throughirrigation, which is more marked in the case of bitterness. This canbe relevant from a marketing point of view: while bitterness is apositive sensory attribute in virgin olive oil, too high a level of bit-terness can instead cause consumers to reject an olive oil. Therefore,the correct use of irrigation will help to regulate the intensity of thisattribute, and hence to promote consumer preference.

5. Conclusions

In terms of olive fruit and oil production from an economicpoint of view, the phases where the maximum attention is neededto avoid conditions of water stress are mainly the flowering, the

setting, and the initial phase of rapid growth of the fruit. Duringthis initial phase of fruit development, adequate water availabilityascertains that the processes of cell division, growth and differen-tiation in the mesocarp will not be adversely affected. Irrigation is

1 orticul

at

postwgfto

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A

B

B

B

B

B

B

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C

D

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F

F

F

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G

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58 F. Orlandi et al. / Scientia H

lso necessary in this period, to counteract the effects of elevatedemperatures and absence of precipitation.

In summary, our detailed analysis of these interactions betweenollen release (olive flowering proxy), meteorological parameters,live fruit yield, and olive oil characteristics over 12 consecutiveeasons in southern Italy establish that one of the most impor-ant aspects is the use of irrigation while monitoring for potentialater stress caused by water deficit in the summer months. Thus,

reater attention needs to be paid on the part of the olive growersor evaluation of the vegetative–productive state of the trees andheir reproductive cycle, and of the hydro-pedological conditionsf the terrain that vary with the seasonal meteorological trends.

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